Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification.

Automatic segmentation and classification of lesions are two clinically significant tasks in the computer-aided diagnosis of skin diseases. Both tasks are challenging due to the nonnegligible lesion differences in dermoscopic images from different patients. In this paper, we propose a novel pipeline to efficiently implement skin lesions' segmentation and classification tasks, which consists of a segmentation network and a classification network. To improve the performance of the segmentation network, we propose a novel module of Multi-Scale Holistic Feature Exploration (MSH) to thoroughly exploit perceptual clues latent among multi-scale feature maps as synthesized by the decoder. The MSH module enables holistic exploration of features across multiple scales to more effectively support downstream image analysis tasks. To boost the performance of the classification network, we propose a novel module of Cross-Modality Collaborative Feature Exploration (CMC) to discover latent discriminative features by collaboratively exploiting potential relationships between cross-modal features of dermoscopic images and clinical metadata. The CMC module enables dynamically capturing versatile interaction effects among cross-modal features during the model's representation learning procedure by discriminatively and adaptively learning the interaction weight associated with each crossmodality feature pair. In addition, to effectively reduce background noise and boost the lesion discrimination ability of the classification network, we crop the images based on lesion masks generated by the best segmentation model. We evaluate the proposed pipeline on the four public skin lesion datasets, where the ISIC 2018 and PH2 are for segmentation, and the ISIC 2019 and ISIC 2020 are combined into a new dataset, ISIC 2019&2020, for classification. It achieves a Jaccard index of 83.31% and 90.14% in skin lesion segmentation, an AUC of 97.98% and an Accuracy of 92.63% in skin lesion classification, which is superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Last but not least, the new model for segmentation utilizes much fewer model parameters (3.3 M) than its peer approaches, leading to a greatly reduced number of labeled samples required for model training, which obtains substantially stronger robustness than its peers.

Multitask Interactive Attention Learning Model Based on Hand Images for Assisting Chinese Medicine in Predicting Myocardial Infarction.

Acute myocardial infarction (AMI) is one of the most serious and dangerous cardiovascular diseases. In recent years, the number of patients around the world has been increasing significantly, among which people under the age of 45 have become the high-risk group for sudden death of AMI. AMI occurs quickly and does not show obvious symptoms before onset. In addition, postonset clinical testing is also a complex and invasive test, which may cause some postoperative complications. Therefore, it is necessary to propose a noninvasive and convenient auxiliary diagnostic method. In traditional Chinese medicine (TCM), it is an effective auxiliary diagnostic strategy to complete the disease diagnosis through some body surface features. It is helpful to observe whether the palmar thenar undergoes hypertrophy and whether the metacarpophalangeal joint is swelling in detecting acute myocardial infarction. Combined with deep learning, we propose a depth model based on traditional palm image (MTIALM), which can help doctors of traditional Chinese medicine to predict myocardial infarction. By building the shared network, the model learns information that covers all the tasks. In addition, task-specific attention branch networks are built to simultaneously detect the symptoms of different parts of the palm. The information interaction module (IIM) is proposed to further integrate the information between task branches to ensure that the model learns as many features as possible. Experimental results show that the accuracy of our model in the detection of metacarpophalangeal joints and palmar thenar is 83.16% and 84.15%, respectively, which are significantly improved compared with the traditional classification methods.

Effective attention-based network for syndrome differentiation of AIDS.

BACKGROUND: Syndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM). Several previous works were devoted to employing the classical algorithms to classify the syndrome and achieved delightful results. However, the presence of ambiguous symptoms substantially disturbed the performance of syndrome differentiation, This disturbance is always due to the diversity and complexity of the patients' symptoms. METHODS: To alleviate this issue, we proposed an algorithm based on the multilayer perceptron model with an attention mechanism (ATT-MLP). In particular, we first introduced an attention mechanism to assign different weights for different symptoms among the symptomatic features. In this manner, the symptoms of major significance were highlighted and ambiguous symptoms were restrained. Subsequently, those weighted features were further fed into an MLP to predict the syndrome type of AIDS. RESULTS: Experimental results for a real-world AIDS dataset show that our framework achieves significant and consistent improvements compared to other methods. Besides, our model can also capture the key symptoms corresponding to each type of syndrome. CONCLUSION: In conclusion, our proposed method can learn these intrinsic correlations between symptoms and types of syndromes. Our model is able to learn the core cluster of symptoms for each type of syndrome from limited data, while assisting medical doctors to diagnose patients efficiently.

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network.

This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary. For this purpose, we modified the HED network architecture to specifically find 'contour-like' objects in mammograms. The proposed framework produced a probability map that can be used to estimate the initial pectoral muscle boundary. Subsequently, we process these maps by extracting morphological properties to find the actual pectoral muscle boundary. Finally, we developed two different post-processing steps to find the actual pectoral muscle boundary. Quantitative evaluation results show that the proposed method is comparable with alternative state-of-the-art methods producing on average values of 94.8 ±â€¯8.5% and 97.5 ±â€¯6.3% for the Jaccard and Dice similarity metrics, respectively, across four different databases.

Cost-effectiveness of electronic- and clinician-delivered screening, brief intervention and referral to treatment for women in reproductive health centers.

AIMS: To determine the cost-effectiveness of electronic- and clinician-delivered SBIRT (Screening, Brief Intervention and Referral to Treatment) for reducing primary substance use among women treated in reproductive health centers. DESIGN: Cost-effectiveness analysis based on a randomized controlled trial. SETTING: New Haven, CT, USA. PARTICIPANTS: A convenience sample of 439 women seeking routine care in reproductive health centers who used cigarettes, risky amounts of alcohol, illicit drugs or misused prescription medication. INTERVENTIONS: Participants were randomized to enhanced usual care (EUC, n = 151), electronic-delivered SBIRT (e-SBIRT, n = 143) or clinician-delivered SBIRT (SBIRT, n = 145). MEASUREMENTS: The primary outcome was days of primary substance abstinence during the 6-month follow-up period. To account for the possibility that patients might substitute a different drug for their primary substance during the 6-month follow-up period, we also considered the number of days of abstinence from all substances. Incremental cost-effectiveness ratios and cost-effectiveness acceptability curves determined the relative cost-effectiveness of the three conditions from both the clinic and patient perspectives. FINDINGS: From a health-care provider perspective, e-SBIRT is likely (with probability greater than 0.5) to be cost-effective for any willingness-to-pay value for an additional day of primary-substance abstinence and an additional day of all-substance abstinence. From a patient perspective, EUC is most likely to be the cost-effective intervention when the willingness to pay for an additional day of abstinence (both primary-substance and all-substance) is less than $0.18 and e-SBIRT is most likely to be the cost-effective intervention when the willingness to pay for an additional day of abstinence (both primary-substance and all-substance) is greater than $0.18. CONCLUSIONS: e-SBIRT could be a cost-effective approach, from both health-care provider and patient perspectives, for use in reproductive health centers to help women reduce substance misuse.

Automated tongue diagnosis on the smartphone and its applications.

Tongue features are important objective basis for clinical diagnosis and treatment in both western medicine and Chinese medicine. The need for continuous monitoring of health conditions inspires us to develop an automatic tongue diagnosis system based on built-in sensors of smartphones. However, tongue images taken by smartphone are quite different in color due to various lighting conditions, and it consequently affects the diagnosis especially when we use the appearance of tongue fur to infer health conditions. In this paper, we captured paired tongue images with and without flash, and the color difference between the paired images is used to estimate the lighting condition based on the Support Vector Machine (SVM). The color correction matrices for three kinds of common lights (i.e., fluorescent, halogen and incandescent) are pre-trained by using a ColorChecker-based method, and the corresponding pre-trained matrix for the estimated lighting is then applied to eliminate the effect of color distortion. We further use tongue fur detection as an example to discuss the effect of different model parameters and ColorCheckers for training the tongue color correction matrix under different lighting conditions. Finally, in order to demonstrate the potential use of our proposed system, we recruited 246 patients over a period of 2.5 years from a local hospital in Taiwan and examined the correlations between the captured tongue features and alanine aminotransferase (ALT)/aspartate aminotransferase (AST), which are important bio-markers for liver diseases. We found that some tongue features have strong correlation with AST or ALT, which suggests the possible use of these tongue features captured on a smartphone to provide an early warning of liver diseases.

Conservative treatment of diffuse infiltrating retinoblastoma: optical coherence tomography-assisted diagnosis and follow-up in three consecutive cases.

BACKGROUND/AIMS: To report conservative therapy in diffuse infiltrating retinoblastoma (DIR) and describe specific optic coherence tomography (OCT) features of the tumour. METHODS: Retrospective review of all DIR cases treated conservatively between 1998 and 2012. RESULTS: Three patients (three eyes) were included, cases 1 and 3 with previous enucleation of the contralateral eye and case 2 with unilateral retinoblastoma referred after prior pars plana vitrectomy with silicone oil. Mean age at diagnosis was 7 years (range 14 months-14 years). Globe and vision preservation (Snellen visual acuity of 12.5/10) was achieved in case 3 with a recurrence-free follow-up of 33 months after first-line thermotherapy followed by salvage intra-arterial chemotherapy (IAC) plus focal treatments. Cases 1 and 2 were enucleated for progressive disease, case 1 after first-line intravenous chemotherapy (IVC) consolidated by focal therapies and salvage treatments given over 8 years of partial remission and case 2 after IAC, brachytherapy and intracameral chemotherapy. Neither showed any high-risk histopathological features, and no adjuvant chemotherapy was necessary. Both patients are alive without metastasis (mean follow-up of >10 years). Pathognomonic features of the tumour were revealed by OCT in all cases, showing infiltration of the ganglion cell layer and horizontal growth over the inner plexiform layer. Complete restoration of the retinal microanatomy was documented after retraction of the tumour following IVC in case 2 and IAC in case 3. CONCLUSION: This is the first report of successful conservative management in DIR. OCT enabled diagnosis, delimitation of the tumour margins and monitoring of the treatment response in this context.

Machine learning techniques for medical diagnosis of diabetes using iris images.

BACKGROUND AND OBJECTIVE: Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for disease diagnosis is reliable and fastest growing field in biomedical. Proposed model is an attempt to evaluate diagnostic validity of an old complementary and alternative medicine technique, iridology for diagnosis of type-2 diabetes using soft computing methods. METHODS: Investigation was performed over a close group of total 338 subjects (180 diabetic and 158 non-diabetic). Infra-red images of both the eyes were captured simultaneously. The region of interest from the iris image was cropped as zone corresponds to the position of pancreas organ according to the iridology chart. Statistical, texture and discrete wavelength transformation features were extracted from the region of interest. RESULTS: The results show best classification accuracy of 89.63% calculated from RF classifier. Maximum specificity and sensitivity were absorbed as 0.9687 and 0.988, respectively. CONCLUSION: Results have revealed the effectiveness and diagnostic significance of proposed model for non-invasive and automatic diabetes diagnosis.

Change in Low Back Movement Patterns After Neurosurgical Intervention for Lumbar Spondylosis.

OBJECTIVES: The purpose of this study was to assess the use of computer-aided combined movement examination (CME) to measure change in low back movement after neurosurgical intervention for lumbar spondylosis and to use a CME normal reference range (NRR) to compare and contrast movement patterns identified from lumbar disk disease, disk protrusion, and nerve root compression cases. METHODS: A test-retest, cohort observational study was conducted. Computer-aided CME was used to record lumbar range of motion in 18 patients, along with pain, stiffness, disability, and health self-report questionnaires. A minimal clinically important difference of 30% was used to interpret meaningful change in self-reports. z Scores were used to compare CME. Post hoc observation included subgrouping cases into 3 discrete pathologic conditions-disk disease, disk protrusion, and nerve root compression-to report intergroup differences in CME. RESULTS: Self-report data indicated that 11, 7, and 10 patients improved by ≥30% in pain, stiffness, and function, respectively. Three patients experienced clinically significant improvement in health survey. A CME pattern reduced in all directions suggested disk disease. Unilaterally restricted movement in side-flexed or extended directions suggested posterolateral disk protrusion with or without ipsilateral nerve root compression. Bilateral restrictions in extension suggested posterior disk protrusion with or without nerve root compression. In 11 of the 18 cases, CME converged toward the NRR after surgery. CONCLUSION: We described the use of CME to identify atypical lumbar movement relative to an NRR. Data from this short-term postoperative study provide preliminary evidence for CME movement patterns suggestive of disk disease, disk protrusion, and nerve root compression.

Quantitative Romberg's test in acute carbon monoxide poisoning treated by hyperbaric oxygen.

OBJECTIVE: The aim of this study was to evaluate whether monitoring of acute carbon monoxide-poisoned (COP) patients by means of quantitative Romberg's test (QR-test) during a hyperbaric oxygen (HBO2) therapy regimen could be a useful supplement in the evaluation of neurological status. METHODS: We conducted a retrospective study (2000-2014) in which we evaluated data containing quantitative sway measurements of acute COP patients (n = 58) treated in an HBO2 regimen. Each patient was tested using QR-test before and after each HBO2 treatment. Data were analyzed using linear mixed models (LMM). In each LMM, sway prior to HBO2 therapy was set as the fixed effect and change in sway after HBO2 therapy was set as the response variable. Patient, treatment number, weight and age were set as random effects for all LMMs. RESULTS: From the LMMs we found that larger values of sway prior to HBO2 produced a negative change in sway. We found no correlation between CO level and sway (P=0.1028; P=0.8764; P=0.4749; P=0.5883). Results showed that loss of visual input caused a significant increase in mean sway (P=0.028) and sway velocity (P⟨0.0001). CONCLUSIONS: The Quantitative Romberg's test is a fast, useful supplement to neurological evaluation and a potential valuable tool for monitoring postural stability during the course of treatment in acute COP patients.

Evaluation of an internet-based speech-in-noise screening test for school-age children.

OBJECTIVE: To evaluate a Dutch online speech-in-noise screening test (in Dutch: "Kinderhoortest") in normal-hearing school-age children. Sub-aims were to study test-retest reliability, and the effects of presentation type and age on test results. DESIGN: An observational cross-sectional study at school. Speech reception thresholds (SRTs) were obtained through the online test in a training condition, and two test conditions: on a desktop computer and smartphone. The order of the test conditions was counterbalanced. STUDY SAMPLE: Ninety-four children participated (5-12 years), of which 75 children were normal-hearing (≤25 dB HL at 0.5 kHz, ≤20 dB HL at 1-4 kHz). RESULTS: There was a significant effect for test order for the two test conditions (first or second test), but not for presentation type (desktop computer or smartphone) (repeated measures analyses, F(1,75) = 12.48, p < 0.001; F(1,75) = 0.01, p = 0.982). SRT significantly improved by age year (first test: 0.25 dB SNR, 95% CI: -0.43 to -0.08, p = 0.004. Second test: 0.29 dB SNR, 95% CI: -0.46 to -0.11; p = 0.002). CONCLUSIONS: The online test shows potential for routine-hearing screening of school-age children, and can be presented on either a desktop computer or smartphone. The test should be evaluated further in order to establish sensitivity and specificity for hearing loss in children.

Structure-Specific Movement Patterns in Patients With Chronic Low Back Dysfunction Using Lumbar Combined Movement Examination.

OBJECTIVE: A test-retest cohort study was conducted to assess the use of a novel computer-aided, combined movement examination (CME) to measure change in low back movement after pain management intervention in 17 cases of lumbar spondylosis. Additionally we desired to use a CME normal reference range (NRR) to compare and contrast movement patterns identified from 3 specific structural pathologic conditions: intervertebral disc, facet joint, and nerve root compression. METHODS: Computer-aided CME was used before and after intervention, in a cohort study design, to record lumbar range of movement along with pain, disability, and health self-report questionnaires in 17 participants who received image-guided facet, epidural, and/or rhizotomy intervention. In the majority of cases, CME was reassessed after injection together with 2 serial self-reports after an average of 2 and 14 weeks. A minimal clinically important difference of 30% was used to interpret meaningful change in self-reports. A CME NRR (n = 159) was used for comparison with the 17 cases. Post hoc observation included subgrouping cases into 3 discrete pathologic conditions, intervertebral disc, facet dysfunction, and nerve root compression, in order to report intergroup differences in CME movement. RESULTS: Seven of the 17 participants stated that a "combined" movement was their most painful CME direction. Self-report outcome data indicated that 4 participants experienced significant improvement in health survey, 5 improved by ≥30% on low back function, and 8 reported that low back pain was more bothersome than stiffness, 6 of whom achieved the minimal clinically important difference for self-reported pain. Subgrouping of cases into structure-specific groups provided insight to different CME movement patterns. CONCLUSION: The use of CME assists in identifying atypical lumbar movement relative to an age and sex NRR. Data from this study, exemplified by representative case studies, provide preliminary evidence for distinct intervertebral disc, facet joint, and nerve root compression CME movement patterns in cases of chronic lumbar spondylosis.

Noninvasive Determination of HV Interval Using Magnetocardiography.

BACKGROUND: The His-ventricular (HV) interval is an important index of atrioventricular conduction, but at present can be reliably measured only during an invasive electrophysiology (EP) study. Magnetocardiography (MCG) is a noninvasive measurement of weak magnetic fields generated by the heart. We compared HV interval noninvasively assessed using MCG with the corresponding values measured directly in an EP study. METHODS: MCG was measured using a 37-channel system inside a magnetically shielded room in patients who had previously undergone an EP study. His-bundle potential was identified in the PR segment after signal averaging. Magnetic field maps representing the spatial distribution of ramp-like signals in the PR segment generated at various instants of time were used to identify His-bundle signals in cases where the deflection representing the His was ambiguous. RESULTS: The study included 23 patients (14 male, nine female) with a wide range of HV intervals measured during EP study (49 ± 17 ms, range 35-120 ms). In 21 (91%) subjects, discernible His-bundle signals are observed in the PR segment of MCG traces. HV intervals measured between the two methods showed a correlation (r2 = 0.87, P < 0.0001) with a mean difference of 5.4 ± 3.2 ms. CONCLUSION: With the use of new criteria to identify the His-bundle deflection in signal-averaged MCG signals, we report a high success rate in noninvasive HV interval measurement and a good agreement with those from EP study. The results encourage the use of MCG as a noninvasive method for measurement of the HV interval.

Personalized Models of Human Atrial Electrophysiology Derived From Endocardial Electrograms.

OBJECTIVE: Computational models represent a novel framework for understanding the mechanisms behind atrial fibrillation (AF) and offer a pathway for personalizing and optimizing treatment. The characterization of local electrophysiological properties across the atria during procedures remains a challenge. The aim of this work is to characterize the regional properties of the human atrium from multielectrode catheter measurements. METHODS: We propose a novel method that characterizes regional electrophysiology properties by fitting parameters of an ionic model to conduction velocity and effective refractory period restitution curves obtained by a s1-s2 pacing protocol applied through a multielectrode catheter. Using an in-silico dataset we demonstrate that the fitting method can constrain parameters with a mean error of 21.9 ± 16.1% and can replicate conduction velocity and effective refractory curves not used in the original fitting with a relative error of 4.4 ± 6.9%. RESULTS: We demonstrate this parameter estimation approach on five clinical datasets recorded from AF patients. Recordings and parametrization took approx. 5 and 6 min, respectively. Models fitted restitution curves with an error of ~ 5% and identify a unique parameter set. Tissue properties were predicted using a two-dimensional atrial tissue sheet model. Spiral wave stability in each case was predicted using tissue simulations, identifying distinct stable (2/5), meandering and breaking up (2/5), and unstable self-terminating (1/5) spiral tip patterns for different cases. CONCLUSION AND SIGNIFICANCE: We have developed and demonstrated a robust and rapid approach for personalizing local ionic models from a clinically tractable.

Design of a Physiology-Sensitive VR-Based Social Communication Platform for Children With Autism.

Individuals with autism are often characterized by impairments in communication, reciprocal social interaction and explicit expression of their affective states. In conventional techniques, a therapist adjusts the intervention paradigm by monitoring the affective state e.g., anxiety of these individuals for effective floor-time-therapy. Conventional techniques, though powerful, are observation-based and face resource limitations. Technology-assisted systems can provide a quantitative, individualized rehabilitation platform. Presently-available systems are designed primarily to chain learning via aspects of one's performance alone restricting individualization. Specifically, these systems are not sensitive to one's anxiety. Our presented work seeks to bridge this gap by developing a novel VR-based interactive system with Anxiety-Sensitive adaptive technology. Specifically, such a system is capable of objectively identifying and quantifying one's anxiety level from real-time biomarkers, along with performance metrics. In turn it can adaptively respond in an individualized manner to foster improved social communication skills. In our present research, we have used Virtual Reality (VR) to design a proof-of-concept application that exposes participants to social tasks of varying challenges. Results of a preliminary usability study indicate the potential of our VR-based Anxiety-Sensitive system to foster improved task performance, thereby serving as a potent complementary tool in the hands of therapist.

Orientation-Independent Catheter-Based Characterization of Myocardial Activation.

Cardiac electrogram (EGM) signals and electrophysiologic (EP) characteristics derived from them such as amplitude and timing are central to the diagnosis and therapeutic management of arrhythmias. Bipolar EGMs are often used but possess polarity and shape dependence on catheter orientation contributing to uncertainty. OBJECTIVE: We describe a novel method to map cardiac activation that resolves signals into meaningful directions and is insensitive to electrode directional effects. METHODS: Multielectrode catheters that span 2- and 3-D space are used to derive local electric field (E-field) signals. A traveling wave model of local EGM propagation motivates a new "omnipolar" reference frame in which to understand EGM E-field signals and provide bipolar component EGMs aligned with these anatomic and physiologic directions. We validate the basis of this technology and determine its accuracy using a saline tank in which we simulate physiologic propagation. RESULTS: Omnipole signals from healthy tissue are nearly free of catheter orientation effects and are constrained by biophysics to consistent morphologies and thus consistent measured amplitudes and timings. Using a 3-D EP mapping system, traveling wave treatment, and omnipolar technology (OT) E-field loops, we derived a new and nearly instantaneous means to determine conduction velocity and activation direction. CONCLUSION: We describe the basis of OT and validate it with ablation and mapping catheters in a saline tank. Finally, we illustrate OT with signals from live subjects. SIGNIFICANCE: OT's novel approach with signal processing and real-time visualization allows for a newly detailed characterization of myocardial activation that is insensitive to catheter orientation.

Mismatch Negativity in First-Episode Schizophrenia: A Meta-Analysis.

Mismatch negativity (MMN) to deviant stimuli is robustly smaller in individuals with chronic schizophrenia compared with healthy controls (Cohen's d > 1.0 or more), leading to the possibility of MMN being used as a biomarker for schizophrenia. However, there is some debate in the literature as to whether MMN is reliably reduced in first-episode schizophrenia patients. For the biomarker to be used as a predictive marker for schizophrenia, it should be reduced in the majority of cases known to have the disease, particularly at disease onset. We conducted a meta-analysis on the fourteen studies that measured MMN to pitch or duration deviants in healthy controls and patients within 12 months of their first episode of schizophrenia. The overall effect size showed no MMN reduction in first-episode patients to pitch-deviants (Cohen's d < 0.04), and a small-to-medium reduction to duration-deviants (Cohen's d = 0.47). Together, this indicates that pitch-deviant MMN is not a candidate biomarker for schizophrenia prediction, while duration-deviant MMN may hold some promise, albeit nearly a third as large an effect as in chronic schizophrenia. Potential causes for discrepancies between studies are discussed.

Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health.

GOAL: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34-1.45 m/s) speeds. METHODS: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. RESULTS: The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking. CONCLUSION: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. SIGNIFICANCE: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement-aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.

Application of Empirical Mode Decomposition Combined With Notch Filtering for Interpretation of Surface Electromyograms During Functional Electrical Stimulation.

The goal of this paper is to demonstrate a novel approach that combines Empirical Mode Decomposition (EMD) with Notch filtering to remove the electrical stimulation (ES) artifact from surface electromyogram (EMG) data for interpretation of muscle responses during functional electrical stimulation (FES) experiments. FES was applied to the rectus femoris (RF) muscle unilaterally of six able bodied (AB) and one individual with spinal cord injury (SCI). Each trial consisted of three repetitions of ES. We hypothesized that the EMD algorithm provides a suitable platform for decomposing the EMG signal into physically meaningful intrinsic mode functions (IMFs) which can be further used to isolate electrical stimulation (ES) artifact. A basic EMD algorithm was used to decompose the EMG signals collected during FES into IMFs for each repetition separately. IMFs most contaminated by ES were identified based on the standard deviation (SD) of each IMF. Each artifact IMF was Notch filtered to filter ES harmonics and added to remaining IMFs containing pure EMG data to get a version of a filtered EMG signal. Of all such versions of filtered signals generated from each artifact IMF, the one with maximum signal to noise ratio (SNR) was chosen as the final output. The validity of the filtered signal was assessed by quantitative metrics, 1) root mean squared error (RMSE) and signal to noise (SNR) ratio values obtained by comparing a clean EMG and EMD-Notch filtered signal from the combination of simulated ES and clean EMG and, 2) using EMG-force correlation analysis on the data collected from AB individuals. Finally, the potential applicability of this algorithm on a neurologically impaired population was shown by applying the algorithm on EMG data collected from an individual with SCI. EMD combined with Notch filtering successfully extracted the EMG signal buried under ES artifact. Filtering performance was validated by smaller RMSE values and greater SNR post filtering. The amplitude values of the filtered EMG signal were seen to be consistent for three repetitions of ES and there was no significant difference among the repetition for all subjects. For the individual with a SCI the algorithm was shown to successfully isolate the underlying bursts of muscle activations during FES. The data driven nature of EMD algorithm and its ability to act as a filter bank at different bandwidths make this method extremely suitable for dissecting ES induced EMG into IMFs. Such IMFs clearly show the presence of ES artifact at different intensities as well as pure artifact free EMG. This allows the application of Notch filters to IMFs containing ES artifact to further isolate the EMG. As a result of such stepwise approach, the extraction of EMG is achieved with minimal data loss. This study provides a unique approach to dissect and interpret the EMG signal during FES applications.

Accuracy and Efficiency of Recording Pediatric Early Warning Scores Using an Electronic Physiological Surveillance System Compared With Traditional Paper-Based Documentation.

Pediatric Early Warning Scores are advocated to assist health professionals to identify early signs of serious illness or deterioration in hospitalized children. Scores are derived from the weighting applied to recorded vital signs and clinical observations reflecting deviation from a predetermined "norm." Higher aggregate scores trigger an escalation in care aimed at preventing critical deterioration. Process errors made while recording these data, including plotting or calculation errors, have the potential to impede the reliability of the score. To test this hypothesis, we conducted a controlled study of documentation using five clinical vignettes. We measured the accuracy of vital sign recording, score calculation, and time taken to complete documentation using a handheld electronic physiological surveillance system, VitalPAC Pediatric, compared with traditional paper-based charts. We explored the user acceptability of both methods using a Web-based survey. Twenty-three staff participated in the controlled study. The electronic physiological surveillance system improved the accuracy of vital sign recording, 98.5% versus 85.6%, P < .02, Pediatric Early Warning Score calculation, 94.6% versus 55.7%, P < .02, and saved time, 68 versus 98 seconds, compared with paper-based documentation, P < .002. Twenty-nine staff completed the Web-based survey. They perceived that the electronic physiological surveillance system offered safety benefits by reducing human error while providing instant visibility of recorded data to the entire clinical team.